Built machine learning models on projected store

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Reference no: EM133752085

Question: The objective

You have just been hired as a consultant for a new and emerging home improvement store called "Tool Time." The year is 2012 and Tool Time has 15 stores and 1500 employees. Tool Time's flagship store is located in Essex county Massachusetts and they have a corporate headquarters in downtown Essex. They have also recently expanded their store footprint. Tool Time now has stores in Rhode Island and New Hampshire. Each of its 15 stores are doing very well in revenue and profit. They have hired you to consult them on potentially expanding and becoming a franchise in the larger New England Region.

Tool Time specializes in home improvement and DIY (Do-it-Yourself) supplies. Its store inventory and customers are similar to that of Home Depot and Lowes. The store footprint of Tool Time is not that of Home Depot and Lowes but they believe they have a competitive advantage in the small communities of the New England area.

You have been provided with a vast amount of data on Lowes and Home Depot including store locations by county, Census 2000 and 2010 data, highway counts per county and property tax data by county. A data scientist working for Tool Time has already provided data visualizations, built machine learning models on projected store sites and done an immense amount of research on its competitors (Home Depot and Lowes).

Reference no: EM133752085

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